The latest comprehensive research agenda in the Journal of Agricultural Education and Extension was published in 2012 (Faure, Desjeux, and Gasselin 2012), and since then there have been quite some developments in terms of biophysical, ecological, climatological, social, political and economic trends that impact farming and the transformation of agriculture and food systems at large as well as new potentially disruptive technologies.
The building of sustainable innovation capabilities in Africa requires an innovation system capable of producing, disseminating and using new knowledge. This paper assesses the process of constructing the National Innovation System (NIS) in Rwanda. It is posited that consensus on and acceptance of the concept of NIS among stakeholders is crucial in the early process of constructing an efficient and dynamic innovation system. Primary empirical data are presented for the case of Rwanda and analyzed in a regional context.
This paper calls for a better integration of place-based, evidence-based and inclusive dimensions in the implementation of the Science, Technology and Innovation (STI) plans and industrial policies in sub-Saharan Africa. To this end, the analysis contrasts with and takes inspiration from the recent and ongoing international experiences in the elaboration of Innovation Strategies for Smart Specialisation (S3).
Technology and innovation are important in addressing complex problems in the agricultural sector in many developing communities. However, ways and mechanisms to integrate them in the agricultural sector are still a challenge due to the lack of clear pathways and trajectories. Value chains are seen as a strong policy instrument to increase profitability in the agricultural sector; there is also debate around whether value chains can be a potential option to organize technology and innovation trajectories in agriculture.
Rather than merely supporting R&D and strengthening innovation systems, the focus of innovation policy is currently shifting towards addressing societal challenges by transforming socio-economic systems. A particular trend within the emerging era of transformative innovation policy is the pursuit of challenge-based innovation missions, such as achieving a 50 % circular economy by 2030. By formulating clear and ambitious societal goals, policy makers are aiming to steer the directionality and adoption of innovation.
Individuals from a diverse range of backgrounds are increasingly engaging in research and development in the field of artificial intelligence (AI). The main activities, although still nascent, are coalescing around three core activities: innovation, policy, and capacity building. Within agriculture, which is the focus of this paper, AI is working with converging technologies, particularly data optimization, to add value along the entire agricultural value chain, including procurement, farm automation, and market access.
Establishing food security remains a global challenge; it is thus a specific objective of the United Nations Sustainable Development Goals for 2030. Successfully delivering productive and sustainable agricultural systems worldwide will form the foundations for overcoming this challenge. Smart agriculture is often perceived as one key enabler when considering the twin objectives of eliminating world hunger and undernourishment. The practical realization, deployment, and adoption of smart agricultural systems remain distant due to a confluence of technological, social, and economic factors.
There have been repeated calls for a ‘new professionalism’ for carrying out agricultural research for development since the 1990s. At the centre of these calls is a recognition that for agricultural research to support the capacities required to face global patterns of change and their implications on rural livelihoods, requires a more systemic, learning focused and reflexive practice that bridges epistemologies and methodologies.
The farmer field school (FFS) concept has been widely adopted, and such schools have the reputation of strengthening farmers’ capacity to innovate. Although their impact has been studied widely, what is involved in their scaling and in their becoming an integral part of agricultural innovation systems has been studied much less. In the case of the Sustainable Tree Crops Programme in Cameroon, we investigate how a public–private partnership (PPP) did not lead to satisfactory widespread scaling in the cocoa innovation system.
The paper explores the strength of social networks in the agricultural innovation systems (AISs) in Ghana and the effect of AISs on adoption of improved farm technology. The paper uses social network analysis (SNA) tools to identify, map and analyze the AISs and the two-stage Heckman selection model. Combining qualitative and quantitative methods allows testing the differential effects of social networks on technology adoption in the Ghananian Plantain Sector